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  • Author or Editor: Rob K. Newsom x
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Rob K. Newsom
and
Robert M. Banta

Abstract

A four-dimensional variational data assimilation (4DVAR) algorithm for retrieval of spatially and temporally resolved velocity and thermodynamic fields within the atmospheric boundary layer (ABL) is described and applied to a coherent Doppler lidar dataset. The adjoint method is used to find the initialization of an ABL model that gives the best fit to radial velocity measurements from the Doppler lidar. The adjoint equations are derived by assuming that subgrid-scale fluxes can be represented as general functions of the resolved-scale rates of strain and potential temperature gradients. For this study, particular attention is paid to the treatment of real measurement error. Radial velocity precision as a function of the signal-to-noise ratio (SNR) is estimated from time series analysis of real fixed beam data, and this information is used in the evaluation of the cost function. The cost function is evaluated by interpolating the model output to the observation coordinates. As a result, the error covariance matrix retains its diagonal structure and the form of the cost function is simplified.

The retrieval method is applied to Doppler lidar data collected under convective conditions during the Cooperative Atmosphere/Surface Exchange Study (CASES-99) field program. The impact of the SNR-dependent measurement error is investigated by comparing a retrieval using equally weighted data to a retrieval using the estimated velocity precisions. At near range the fields are well correlated. However, at longer range, as the velocity precision exceeds the standard deviation of the measurements, the correlation decreases rapidly. Furthermore, retrievals using equally weighted data produce higher variances.

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Rob K. Newsom
and
Robert M. Banta

Abstract

A series of trials are performed to evaluate the sensitivity of a 4DVAR algorithm for retrieval of microscale wind and temperature fields from single-Doppler lidar data. These trials use actual Doppler lidar measurements to examine the sensitivity of the retrievals to changes in 1) the prescribed eddy diffusivity profile, 2) the first-guess or base-state virtual potential temperature profile, 3) the phase and duration of the assimilation period, and 4) the grid resolution.

The retrieved fields are well correlated among trials over a reasonable range of variation in the eddy diffusivity coefficients. However, the retrievals are quite sensitive to changes in the gradients of the first-guess or base-state virtual potential temperature profile, and to changes in the phase (start time) and duration of the assimilation period. Retrievals using different grid resolutions exhibit similar larger-scale structure, but differ considerably in the smaller scales. Increasing the grid resolution significantly improved the fit to the radial velocity measurements, improved the convergence rate, and produced variances and fluxes that were in better agreement with tower-based sonic anemometers.

Horizontally averaged variance and heat flux profiles derived from the final time steps of all the retrievals are similar to typical large-eddy-simulation (LES) results for the convective boundary layer. However, all retrieved statistics show significant nonstationarity because fluctuations in the initial state tend to be confined within the boundaries of the scan.

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Rob K. Newsom
,
David D. Turner
, and
John E. M. Goldsmith

Abstract

This study investigates the accuracy and calibration stability of temperature profiles derived from an operational Raman lidar over a 2-yr period from 1 January 2009 to 31 December 2010. The lidar, which uses the rotational Raman technique for temperature measurement, is located at the U.S. Department of Energy's Atmospheric Radiation Measurement site near Billings, Oklahoma. The lidar performance specifications, data processing algorithms, and the results of several test runs are described. Calibration and overlap correction of the lidar is achieved using simultaneous and collocated radiosonde measurements. Results show that the calibration coefficients exhibit no significant long-term or seasonal variation but do show a distinct diurnal variation. When the diurnal variation in the calibration is not resolved the lidar temperature bias exhibits a significant diurnal variation. Test runs in which only nighttime radiosonde measurements are used for calibration show that the lidar exhibits a daytime warm bias that is correlated with the strength of the solar background signal. This bias, which reaches a maximum of ~2.4 K near solar noon, is reduced through the application of a correction scheme in which the calibration coefficients are parameterized in terms of the solar background signal. Comparison between the corrected lidar temperatures and the noncalibration radiosonde temperatures show a negligibly small median bias of −0.013 K for altitudes below 10 km AGL. The corresponding root-mean-square difference profile is roughly constant at ~2 K below 6 km AGL and increases to about 4.5 K at 10 km AGL.

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Tyler J. Thorsen
,
Qiang Fu
,
Rob K. Newsom
,
David D. Turner
, and
Jennifer M. Comstock

Abstract

A feature detection and extinction retrieval (FEX) algorithm for the Atmospheric Radiation Measurement Program’s (ARM) Raman lidar (RL) has been developed. Presented here is Part I of the FEX algorithm: the detection of features including both clouds and aerosols. The approach of FEX is to use multiple quantities— scattering ratios derived using elastic and nitrogen channel signals from two fields of view, the scattering ratio derived using only the elastic channel, and the total volume depolarization ratio—to identify features using range-dependent detection thresholds. FEX is designed to be context sensitive with thresholds determined for each profile by calculating the expected clear-sky signal and noise. The use of multiple quantities provides complementary depictions of cloud and aerosol locations and allows for consistency checks to improve the accuracy of the feature mask. The depolarization ratio is shown to be particularly effective at detecting optically thin features containing nonspherical particles, such as cirrus clouds. Improvements over the existing ARM RL cloud mask are shown. The performance of FEX is validated against a collocated micropulse lidar and observations from the Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) satellite over the ARM Darwin, Australia, site. While the focus is on a specific lidar system, the FEX framework presented here is suitable for other Raman or high spectral resolution lidars.

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Yelena L. Pichugina
,
Sara C. Tucker
,
Robert M. Banta
,
W. Alan Brewer
,
Neil D. Kelley
,
Bonnie J. Jonkman
, and
Rob K. Newsom

Abstract

Quantitative data on turbulence variables aloft—above the region of the atmosphere conveniently measured from towers—have been an important but difficult measurement need for advancing understanding and modeling of the stable boundary layer (SBL). Vertical profiles of streamwise velocity variances obtained from NOAA’s high-resolution Doppler lidar (HRDL), which have been shown to be approximately equal to turbulence kinetic energy (TKE) for stable conditions, are a measure of the turbulence in the SBL. In the present study, the mean horizontal wind component U and variance σ 2 u were computed from HRDL measurements of the line-of-sight (LOS) velocity using a method described by Banta et al., which uses an elevation (vertical slice) scanning technique. The method was tested on datasets obtained during the Lamar Low-Level Jet Project (LLLJP) carried out in early September 2003, near the town of Lamar in southeastern Colorado.

This paper compares U with mean wind speed obtained from sodar and sonic anemometer measurements. The results for the mean U and mean wind speed measured by sodar and in situ instruments for all nights of LLLJP show high correlation (0.71–0.97), independent of sampling strategies and averaging procedures, and correlation coefficients consistently >0.9 for four high-wind nights, when the low-level jet speeds exceeded 15 m s−1 at some time during the night. Comparison of estimates of variance, on the other hand, proved sensitive to both the spatial and temporal averaging parameters. Several series of averaging tests are described, to find the best correlation between TKE calculated from sonic anemometer data at several tower levels and lidar measurements of horizontal-velocity variance σ 2 u . Because of the nonstationarity of the SBL data, the best results were obtained when the velocity data were first averaged over intervals of 1 min, and then further averaged over 3–15 consecutive 1-min intervals, with best results for the 10- and 15-min averaging periods. For these cases, correlation coefficients exceeded 0.9. As a part of the analysis, Eulerian integral time scales (τ) were estimated for the four high-wind nights. Time series of τ through each night indicated erratic behavior consistent with the nonstationarity. Histograms of τ showed a mode at 4–5 s, but frequent occurrences of larger τ values, mostly between 10 and 100 s.

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